Hepatocellular carcinoma (HCC), the most prevalent malignant liver cancer, has seen an increasing incidence rate over the last decade. Segmental liver resection and liver transplantation are curative options shown to be effective when the patient meets certain criteria, which rely on a sensitive and early detection and characterization of HCC lesions with imaging. Because the liver may be too diseased to tolerate a large resection or due to insufficient numbers of donor livers, these treatments are not available for a major part of the patient population. For these patients, other treatment options are considered including systemic chemotherapy. While treatment response is conventionally measured based on tumor size change, this may take several months to occur. A sensitive and early measurement of treatment response is therefore expected to significantly impact treatment as well. It is in this context that quantitative MR imaging plays an increasing role. High temporal and spatial resolution dynamic imaging has been introduced into contrast-enhanced liver MRI to overcome bolus-timing problems and to capture the desired enhancement phases traditionally used for diagnosis. However, current methods are still limited to breath-holds or very moderate amounts of respiratory motion. Additionally, the reader is presented with a large number of images, from which a manual selection of the typical arterial, portal-venous and delayed phases is necessary. Our long-term goal is to develop MRI into a robust method for the diagnosis of liver disease. As part of this goal, it is the objective of this application is to transform dynami liver contrast enhanced MRI into a quantitative measure of liver cancer presence and progression. Our central hypothesis is that a quantitative kinetic parametric map (KPM) of hepatic arterial blood flow and bolus arrival time estimated directly from the raw MRI data will have a better diagnostic performance for detecting HCC than conventional multiple phase MRI. The rationale for the proposed research is that the resulting KPMs enable a compact representation of the contrast enhancement behavior that is reproducible and lends itself to intra- and inter-patient comparisons. We plan to test our hypothesis by pursuing the following aims: (1) develop an imaging method that maps the acquired MR data directly onto quantitative kinetic parametric maps, and (2) evaluate the diagnostic performance of the developed quantitative method in patients undergoing liver transplantation. The expected outcome of this research of this research is a better method for characterizing liver tumors during initial detection and follow-up. Such a technique is likely to have a positive impact, since it is expected to allow a quantitative measure of cancer burden, allow an early measurement of treatment success or failure and reduce the need for liver biopsy.